2004Unpublished venueRequires access

A first-order superscalar processor model

Tejas S. Karkhanis, James E. Smith

Open publisher page 128 citations

Abstract

A proposed performance model for superscalar processors consists of: 1) a component that models the relationship between instructions issued per cycle and the size of the instruction window under ideal conditions; and 2) methods for calculating transient performance penalties due to branch mispredictions, instruction cache misses, and data cache misses. Using trace-derived data dependence information, data and instruction cache miss rates, and branch miss-prediction rates as inputs, the model can arrive at performance estimates for a typical superscalar processor that are within 5.8% of detailed simulation on average and within 13% in the worst case. The model also provides insights into the workings of superscalar processors and long-term microarchitecture trends such as pipeline depths and issue widths.

About this research paper

What this paper is about

A proposed performance model for superscalar processors consists of: 1) a component that models the relationship between instructions issued per cycle and the size of the instruction window under ideal conditions; and 2) methods for calculating transient performance penalties due to branch mispredictions, instruction cache misses, and data cache misses. Using trace-derived data dependence information, data and instruction cache miss rates, and branch miss-prediction rates as inputs, the model can arrive at performance estimates for a typical superscalar processor that are within 5.8% of detailed simulation on average and within 13% in the worst case. The model also provides insights into the workings of superscalar processors and long-term microarchitecture trends such as pipeline depths and issue widths.

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OpenAlex reports 128 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

A proposed performance model for superscalar processors consists of: 1) a component that models the relationship between instructions issued per cycle and the size of the instruction window under ideal conditions; and 2) methods for calculating transient performance penalties due to branch mispredictions, instruction cache misses, and data cache misses. Using trace-derived data dependence information, data and instruction cache miss rates, and branch miss-prediction rates as inputs, the model can arrive at performance estimates for a typical superscalar processor that are within 5.8% of detailed simulation on average and within 13% in the worst case. The model also provides insights into the workings of superscalar processors and long-term microarchitecture trends such as pipeline depths and issue widths.

Key concepts: Superscalar, Computer science, Parallel computing, Pipeline (software), Branch predictor, Cache, Pipeline burst cache, Microarchitecture

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